Company Description
Visa is a world leader in payments and technology, with over 259 billion payments transactions flowing safely between consumers, merchants, financial institutions, and government entities in more than 200 countries and territories each year. Our mission is to connect the world through the most innovative, convenient, reliable, and secure payments network, enabling individuals, businesses, and economies to thrive while driven by a common purpose – to uplift everyone, everywhere by being the best way to pay and be paid.
Make an impact with a purpose-driven industry leader. Join us today and experience Life at Visa.
Job Description
We’re hiring a Data and Model Operations Engineer experienced in building big data pipelines and proficient in managing ML models in a production setting. The role involves overseeing the entire lifecycle of ML applications, from development to deployment and monitoring. You’ll be part of a global engineering team, collaborating with stakeholders in consulting, data science, and data engineering. Your responsibilities will include strategic planning, business analysis, and technical guidance in data engineering and architecture solutions. You’ll manage our data engineering assets globally, build relationships with consulting partners, and lead data engineering and data science teams. The ideal candidate will have a background in data engineering, data science, and machine learning engineering.
This position is based in Visa’s offices in Bangalore, India, and presents an excellent opportunity for those looking to make a significant impact in the field of Data Engineering and Model Operations.
Essential Functions
- Requirement Analysis: Understand and translate business needs into data models supporting long-term solutions.
- Data Modeling: Work with the Business team to implement data strategies, build data flows and develop conceptual data models.
- Data Pipeline Design: Create robust and scalable data pipelines and data products in a variety of domains.
- Data Integration: Develop and maintain data lakes by acquiring data from primary and secondary sources and build scripts that will make our data evaluation process more flexible or scalable across data sets.
- Testing: Define and manage the data load procedures to reject or discard datasets or data points that do not meet the defined business rules and quality thresholds.
- Deployment: Implement data strategies and develop physical data models, along with the development teams, data analyst teams and information system team to ensure robust operational data management systems.
- Understanding of and ability to Implement Data Engineering principles and best practices.
- Building MLOps Pipelines: Develop, construct, Test, and maintain architectures such as databases and large-scale processing systems to manage the ML lifecycle.
- Developing and Retraining ML Models: Develop machine learning models and retrain existing models based on evolving data trends.
- Creating Data Pipelines: Create scalable and real-time data pipelines for machine learning workflows.
- Collaboration: Collaborate with data scientists and ML engineers to understand their needs and assist with ML lifecycle processes.
- Stakeholder Management: Work closely with stakeholders across the organization to understand their needs and deliver solutions that meet their requirements.
- Continuous Improvement: Continually improve ongoing reporting and analysis processes, automating or simplifying self-service support for customers.
This is a hybrid position. Hybrid employees can alternate time between both remote and office. Employees in hybrid roles are expected to work from the office 2-3 set days a week (determined by leadership/site), with a general guidepost of being in the office 50% or more of the time based on business needs.
Qualifications
Technical Skills:
• Extensive experience in big data tools: Hadoop, Hive, and Spark.
• Proficiency in Scala, Python, SQL, and PySpark.
• Experience with Unix/Linux systems with scripting experience in Bash.
• Experience with data pipeline and workflow management tools like Airflow, etc.
• Proficiency in Machine Learning, Deep Learning, and related technologies.
• Experience working in building and integrating the code in the defined CI/CD framework using git.
• Proficient in some or all of the following techniques – Linear & Logistic Regression, Decision Trees, XG Boost, Random Forests, K-Nearest Neighbors, etc.
• Experience working with large scale data ingestion, processing, and storage in distributed computing environments / big data platforms (Hadoop) as well as common database systems and value stores (Parquet, Avro, HBase, etc.).
• Familiarity with Python-based data science libraries: iPython, sci-kit learn, Pandas.
• Familiarity with both common computing environments (e.g., Linux, Shell Scripting) and commonly used IDE’s (Jupyter Notebooks).
• Understanding of data warehousing and ETL techniques.
• Experience with Python or R for data analysis is a plus.
• Experience with relational SQL and NoSQL databases, including Postgres and Cassandra.
• Experience with cloud services AWS or Azure.
• Experience with stream-processing systems: Kafka, Spark-Streaming, etc.
• Strong analytic skills related to working with structured & unstructured datasets.
• Familiarity with Agile development methodologies.
• Strong quantitative skills, with a degree in a field such as Statistics, Mathematics, Computer Science, or related field.
Qualifications
• 5+ years of work experience with a bachelor’s degree or at least 3+ years of work experience with an Advanced degree (e.g. Master’s, MBA, JD, MD) or 1+ years of work experience with a PhD degree
• Exposure to Financial Services/ Payments Industry
Other Skills
• Strong problem-solving skills.
• Excellent communication skills.
• Ability to work in a team.
• Detail-oriented and excellent organizational skills.
• Strong understanding of machine learning principles and best practices.
• Ability to translate complex data into understandable, actionable insights.
Additional Information
Visa is an EEO Employer. Qualified applicants will receive consideration for employment without regard to race, color, religion, sex, national origin, sexual orientation, gender identity, disability or protected veteran status. Visa will also consider for employment qualified applicants with criminal histories in a manner consistent with EEOC guidelines and applicable local law.
